Modulation transfer function (MTF) is a well defined and accepted method of measuring image sharpness. The slanted
edge test, as defined in ISO12233 is a standard method of calculating MTF, and is widely used for lens alignment and
auto-focus algorithm verification. However, there are a number of challenges which should be considered when
measuring MTF in cameras with fisheye lenses. Due to trade-offs related Petzval curvature, planarity of the optical
plane is difficult to achieve in fisheye lenses. It is therefore critical to have the ability to accurately measure sharpness
throughout the entire image, particularly for lens alignment. One challenge for fisheye lenses is that, because of the
radial distortion, the slanted edges will have different angles, depending on the location within the image and on the
distortion profile of the lens. Previous work in the literature indicates that MTF measurements are robust for angles
between 2 and 10 degrees. Outside of this range, MTF measurements become unreliable. Also, the slanted edge itself
will be curved by the lens distortion, causing further measurement problems. This study summarises the difficulties in
the use of MTF for sharpness measurement in fisheye lens cameras, and proposes mitigations and alternative methods.
In this paper we propose a methodology for TV set verification, intended for detecting picture quality degradation and
functional failures within a TV set. In the proposed approach we compare the TV picture captured from a TV set under
investigation with the reference image for the corresponding TV set in order to assess the captured picture quality and
therefore, assess the acceptability of TV set quality. The methodology framework comprises a logic block for designing
the verification process flow, a block for TV set quality estimation (based on image quality assessment) and a block for
generating the defect tracking database. The quality assessment algorithm is a full-reference intra-frame approach which
aims at detecting various digital specific-TV-set picture degradations, coming from TV system hardware and software
failures, and erroneous operational modes and settings in TV sets. The proposed algorithm is a block-based scheme
which incorporates the mean square error and a local variance between the reference and the tested image. The artifact
detection algorithm is shown to be highly robust against brightness and contrast changes in TV sets. The algorithm is
evaluated by performance comparison with the other state-of-the-art image quality assessment metrics in terms of
detecting TV picture degradations, such as illumination and contrast change, compression artifacts, picture
misalignment, aliasing, blurring and other types of degradations that are due to defects within the TV set video chain.
A new fuzzy-rule-based algorithm for the denoising of video sequences corrupted with additive Gaussian noise is presented. The proposed method constitutes a fuzzy-logic-based improvement of a recent detail and motion adaptive multiple class averaging filter (MCA). The method is first explained in the pixel domain for grayscale sequences, and is later extended to the wavelet domain and to color sequences. Experimental results show that the noise in digital image sequences is efficiently removed by the proposed fuzzy motion and detail adaptive video filter (FMDAF), and that the method outperforms other state of the art filters of comparable complexity on different video sequences.
Optical coherence tomography produces high resolution medical images based on spatial and temporal coherence
of the optical waves backscattered from the scanned tissue. However, the same coherence introduces
speckle noise as well; this degrades the quality of acquired images.
In this paper we propose a technique for noise reduction of 3D OCT images, where the 3D volume is
considered as a sequence of 2D images, i.e., 2D slices in depth-lateral projection plane. In the proposed
method we first perform recursive temporal filtering through the estimated motion trajectory between
the 2D slices using noise-robust motion estimation/compensation scheme previously proposed for video
denoising. The temporal filtering scheme reduces the noise level and adapts the motion compensation on
it. Subsequently, we apply a spatial filter for speckle reduction in order to remove the remainder of noise
in the 2D slices. In this scheme the spatial (2D) speckle-nature of noise in OCT is modeled and used for
spatially adaptive denoising. Both the temporal and the spatial filter are wavelet-based techniques, where
for the temporal filter two resolution scales are used and for the spatial one four resolution scales.
The evaluation of the proposed denoising approach is done on demodulated 3D OCT images on different
sources and of different resolution. For optimizing the parameters for best denoising performance fantom
OCT images were used. The denoising performance of the proposed method was measured in terms of
SNR, edge sharpness preservation and contrast-to-noise ratio. A comparison was made to the state-of-the-art
methods for noise reduction in 2D OCT images, where the proposed approach showed to be advantageous
in terms of both objective and subjective quality measures.
Video sequences are often distorted by noise and are usually found in
interlaced format throughout the TV video chain. Joint noise reduction and deinterlacing of such image sequences in interlaced format is a challenging task, required by various applications such as video surveillance or video quality improvement for TFT TV-sets, projectors, plasma panels and LCD screens which display video in progressive format. In this paper, we propose a novel, wavelet-domain joint deinterlacer and denoiser. We apply the wavelet decomposition to each field of the interlaced sequence and perform spatio-temporal interpolation and denoising in the wavelet domain in a motion-compensated manner. Hence, the processed wavelet bands are not only denoised but also have twice as more horizontal lines. We split these full resolution processed bands into odd and even lines, on which we separately perform two inverse wavelet transforms. Each of these inverse transforms uses the same filter bank that is the dual of the decomposition filter bank. As a final step the odd and even lines being the outputs of the two inverse wavelet transforms are merged in order to produce the denoised sequence in progressive format. The results of the proposed algorithm show good performance of the proposed joint deinterlacing and denoising scheme, for various noise levels tested. Specifically, the noise is efficiently removed and the spatio-temporal interpolation is performed in a superior manner without introducing significant visible artifacts.
We propose a fuzzy logic recursive scheme for motion detection and spatiotemporal filtering that can deal with the Gaussian noise and unsteady illumination conditions in both the temporal and spatial directions. Our focus is on applications concerning tracking and denoising of image sequences. We process an input noisy sequence with fuzzy logic motion detection to determine the degree of motion confidence. The proposed motion detector combines the membership of the temporal intensity changes, appropriately using fuzzy rules, where the membership degree of motion for each pixel in a 2-D sliding window is determined by a proposed membership function. Both the fuzzy membership function and the fuzzy rules are defined in such a way that the performance of the motion detector is optimized in terms of its robustness to noise and unsteady lighting conditions. We simultaneously perform tracking and recursive adaptive temporal filtering, where the amount of filtering is inversely proportional to the confidence in the existence of motion. Finally, temporally filtered frames are further processed by a proposed spatial filter to obtain a denoised image sequence. Our main contribution is a robust novel fuzzy recursive scheme for motion detection and temporal filtering. We evaluate the proposed motion detection algorithm using two criteria: (1) robustness to noise and to changing illumination conditions and (2) motion blur in temporal recursive denoising. Additionally, we make comparisons in terms of noise reduction with other state of the art video denoising techniques.
In this paper, we propose an advanced wavelet domain denoising scheme for the Gaussian noise reduction in color video. In the proposed method, we perform the wavelet transform of the luminance component (Y) in full resolution and the wavelet transform of chrominance information (U and V) in a subsampled resolution (2:1) in order to extract edges belonging to luminance and chrominance gradients (in horizontal and vertical directions). We use the low-pass (approximation) subbands of the chrominance channels together with detail (wavelet) bands for recursive motion estimation and adaptive temporal filtering. The final part of the proposed filter is a spatial filter out of the recursive loop. The noise level is monitored and detected automatically, by the gradient histogram approach for each channel separately and the estimated noise variance is used for adapting the algorithm. The results on color video show good video denoising performance for different noise levels. In comparison to the other color video denoising methods, the method performs better both from subjective (visually) and objective point of view. Also, the estimated motion vector field is quite robust against noise and this is useful in other applications such as tracking.
In this paper we propose a fuzzy logic recursive scheme for motion detection and temporal filtering that can deal with the Gaussian noise and unsteady illumination conditions both in temporal and spatial direction. Our focus is on applications concerning tracking and denoising of image sequences. We process an input noisy sequence with fuzzy logic motion detection in order to determine the degree of motion confidence. The proposed motion detector combines the membership degree appropriately using defined fuzzy rules, where the membership degree of motion for each pixel in a 2D-sliding-window is determined by the proposed membership function. Both fuzzy membership function and fuzzy rules are defined in such a way that the performance of the motion detector is optimized in terms of its robustness to noise and unsteady lighting conditions. We perform simultaneously tracking and recursive adaptive temporal filtering, where the amount of filtering is inversely proportional to the confidence with respect to the existence of motion. Finally, temporally filtered frames are further processed by the proposed spatial filter in order to obtain denoised image sequence. The main contribution of this paper is the robust novel fuzzy recursive scheme for motion detection and temporal filtering. We evaluate the proposed motion detection algorithm using two criteria: robustness to noise and changing illumination conditions and motion blur in temporal recursive denoising. Additionally, we make comparisons in terms of noise reduction with other state of the art video denoising techniques.
In this paper we develop an advanced spatio-temporal wavelet domain filtering algorithm which is suitable for hardware
implementation, we implement it in the Field Programmable Gate Arrays (FPGA) and report the results of real-time processing.
The wavelet decomposition in our implementation is non-decimated with three decomposition levels and with a Daubechies' minimum phase orthogonal wavelet. Noise reduction is implemented with spatially adaptive Bayesian wavelet shrinkage. In the next filtering stage, a motion detector controls selective, recursive averaging of pixel intensities over time. The algorithm is customized for the hardware implementation and is realized in FPGA. The standard composite television video stream is digitalized and used as source for real-time video sequences. The results demonstrate the effectiveness of the developed scheme for real time video processing.
Non-linear techniques for denoising images and video are known to be
superior to linear ones. In addition video denoising using spatio-temporal information is considered to be more efficient compared with the use of just temporal information in the presence of fast motion and low noise. Earlier, we introduced a 3-D extension of the K-nearest neighbor filter and have investigated its properties.
In this paper we propose a new, motion- and detail-adaptive filter,
which solves some of the potential drawbacks of the non-adaptive version: motion caused artifacts and the loss of fine details and texture. We also introduce a novel noise level estimation
technique for automatic tuning of the noise-level dependent parameters.
The results show that the adaptive K-nearest neighbor filter outperforms the none-adaptive one, as well as some other
state-of-the-art spatio-temporal filters such as the 3D
alpha-trimmed mean and the state-of-the-art rational filter by Ramponi from both a PSNR and visual quality point of
We develop a sequential wavelet domain and temporal filtering scheme, with jointly optimized parameters, which results in high-quality video denoising over a large range of noise levels. In this scheme, spatial filtering is performed by a spatially adaptive Bayesian wavelet shrinkage in a redundant wavelet representation. In the next filtering stage, a motion detector controls selective, recursive averaging of pixel intensities over time. The results demonstrate that the proposed filter outperforms recent single-resolution representatives as well as some recent motion-compensated wavelet based video filters.
We also analyze important practical issues for possible industrial applications. In particular, we investigate the performance degradations that result from making the wavelet domain filtering part less complex, by removing the redundancy of the representation and/or by replacing a sophisticated spatially adaptive shrinkage method by soft-thresholding.