Even though substantial progress has been made in super resolution research, many issues regarding robust sub-pixel
estimation and fast implementation of feature preserving restoration still exist. To obtain more reliable sub-pixel
information, we proposed to correct mis-aligned sub-pixels by motion vector (MV) processing based on hierarchical
block partition and weighted vector median filtering (WVMF). Two indices - relative displaced frame difference and
motion vector similarity degree - are computed and compared with trained thresholds to classify the motion blocks into
reliable and unreliable groups. Then the unreliable blocks are divided into four sub-blocks with their motion vector
processed by WVMF based on the reliability information of their neighborhood blocks. To preserve the local features
such as edge direction, strength as well as its spread region, anisotropic kernels are learned from local gradient fields to
represent edge information. Finally, a kernel constrained projection is established for restoring high resolution frames.
The experimental results show that the proposed algorithm preserves important features in the images and outperforms
the traditional POCS method.
Many advance image processing, like segmentation and recognition, are based on contour extraction which usually lack of ability to allocate edge precisely in the image of heavy noise with low computation burden. For such problem, in this paper, we proposed a new approach of edge detection based on pyramid-structure wavelet transform. In order to suppress noise and keep good continuity of edge, the proposed edge representation considered both inter-correlations across the multi-scales and intra-correlations within the single-scale. The former one is described by point-wise singularity. The later one is described by the magnitude and ratio of wavelet coefficients in different sub-bands. Based on such edge modeling, the edge point allocation is then complemented in wavelet domain by synthesizing the edge information in multi-scales. The experimental results shows that our approaches achieve the pixel-level edge detection with strong resistant against noise due to scattering in water.
The existing methods for texture modeling include co-occurrence statistics, filter banks and random fields. However most of these methods lack of capability to characterize the different scale of texture effectively. In this paper, we propose a texture representation which combines local scale feature, amplitude and phase of wavelet modules in multi-scales. The self-similarity of texture is not globally uniform and could be measured in both correlations across the multi-scale and statistical feature within a single-scale. In our approach, the local scale feature is represented by optimal scale obtained through the evolution of wavelet modulus across multi-scales. Then, for all the blocks of the same optimal scale, the statistical measurement of amplitude is extracted to represent the energy within the corresponding frequency
band; the statistical measurement of the phase of modulus is extracted to represent the texture's orientation. Our experiment indicates that, in the proposed texture representation the separability of different texture patterns is larger than the one of the traditional features.
In order to improve the quality of image with super-resolution reconstruction, a method based on motion estimation error and edge constraint was proposed. Under the condition of data consistency and amplitude restriction, the motion estimation error was analyzed, with its variance being calculated; meanwhile, in order to suppress the ringing artifacts, edge constraint was adopted and a method based clustering for judging the edge's direction was proposed. The experimental results show that the performance of the this algorithm is better than the traditional linear interpolation and method without considering motion estimation error both in vision effect and peak signal to noise ratio.
Digital watermarking is an efficacious technique to protect the copyright and ownership of digital information. But in the traditional methods of watermarking images, the information of original image will be distorted more or less. Facing this problem, a new watermarking approach, zero-watermarking technique, is proposed. The zero-watermarking approach changes the traditional doings that watermarking is embedded into images, and makes the watermarked image distortion-free. Zero-watermarking can successfully solve the conflict between invisibility and robustness. In this paper, a digital image zero-watermarking method based on discrete wavelet transform and chaotic modulation is proposed.
The zero-watermarking algorithm based on DWT and chaos modulation consists of watermark embedding and detecting processes.
The watermark embedding process is as follow:
First, the original image is decomposed to three-level in wavelet domain. Second, some low frequency wavelet coefficients of original image are selected. The selection of the wavelet coefficients is random by chaotic modulation. Third, the character of coefficients selected is used to construct the character watermark. For each coefficient, in comparison with the adjacent coefficient, we can get the character watermark.
The watermark extracting process is invert process. The location of the coefficients being extracted is also determined by chaotic sequence.
The experimental results show that the watermarking method is invisible and robust against some image processing such as median filtering, JPEG compression, additive Gaussian noise, cropping and rotation attacks and so on. If the initial value of chaos is unknown, the character watermarking can't be extracted correctly.