We present a new digital image interpolation technique and analyze its use in digital photography. Our main goal is generate interpolated images with high quality, eliminating or drastically reducing distortions caused by reconstruction error. Our initial hypothesis is that interpolation techniques with frequency response closer to ideal reconstruction can reduce or eliminate reconstruction error, improving image quality. The proposed technique is a new 2D third-order filter, implementing discrete convolution between image samples and interpolation coefficients that are different for each interpolated pixel. To validate the proposed technique, we compared it with piecewise constant, linear and cubic interpolators.
We present and analyze a new digital image manipulation method. Our main goals are to optimize the use of resources for image generation, storage, transmission, processing and display, and to display images with high quality. The proposed method consists on generating, storing, transmitting and processing images with fewer image samples (manipulation resolution), than screen pixels (display resolution). The display resolution is greater than manipulation resolution, and in the last stage of the proposed method, image display stage, we use some high quality reconstruction technique to generate these new pixels. In this work, we use the Two Dimensional Normalized Sampled Finite Sinc Reconstructor (NSFSR 2-D). We make qualitative and quantitative analyses of the proposed and currently used methods, and observe two important situations: Using the same image display resolution in both methods, the proposed has a smaller image manipulation resolution and resources usage, and image display quality is similar. Using the same image manipulation resolution and different image display resolutions, both methods have the same image manipulation resources usage and the proposed method has a much better image display quality. We conclude that the proposed image manipulation method achieves a better overall image quality, and reduces drastically the resources usage, like network bandwidth, processing and storage capacity. Thus, our main goals were achieved.
This paper analyzes an image reconstruction technique, called 2D normalized sampled finite sinc reconstructor (NSFSR 2D), different from the currently used image reconstruction technique modeled as a 2D zero order reconstructor (ZOR 2D). The main goal of this work is to eliminate or drastically reduce the jagged effect caused by the reconstruction error. The proposed reconstructor has a behavior closer to the ideal reconstructor used in the original sampling theorem. Some tests were done in space and frequency domains comparing this reconstructor to the currently used. We conclude that NSFSR 2D is much better than the ZOR 2D. ALthough some improvements should be done, the results so far are expressive and promising.
This paper shows an architectural proposal for an application specific integrated circuit (ASIC) designed to perform image reconstruction in real time. This architecture implements in hardware the reconstruction technique, called 2D normalized sampled finite sinc reconstructor (NSFSR 2D), which has been formerly proposed and implemented in software. We develop an ASIC that implements NSFSR 2D technique as a dedicated static pipeline architecture. We model and simulate this architecture using VHDL hardware description language. Based on analysis of the validation results, we conclude that the proposed architecture implements the NSFSR 2D correctly and is optimized in performance when compared with a software-based implementation.
This paper analyzes the jagged effect that occurs in raster image display devices.Our main goal is to identify the true causes of the jagged effect, and the initial hypothesis is that between sampling and reconstruction errors the latter is the only cause. The developed methodology used to perform the research about the true causes of the jagged effect is based on the creation of four test situations where we control the occurrence of the two possible causes to be tested. In each test situation we investigate the occurrence of it, sampling error and reconstruction error in the time and frequency domains. The final conclusion based on the result of these analyses is that between the reconstruction and sampling errors, the reconstruction is the only one that produce the jagged effect.