Restoration of Realistic Motion-Blurred Images

Restoration of more realistic motion-blurred images, specifically limited to horizontally or vertically blurred images. Reference: "Removing Camera Shake from a Single Image," SIGGRAPH 2006.

In this research domain, the restoration of motion-blurred images represents a significant challenge. Through image processing and analysis techniques such as blind deconvolution and PSF estimation algorithms, we can reconstruct the original sharp images from blurred versions. This technology holds substantial value for researchers and professionals working in image processing applications. This paper references the seminal work "Removing Camera Shake from a Single Image" (SIGGRAPH 2006), which provides a methodology for restoring realistic motion-blurred images using advanced computational photography techniques.

The research particularly focuses on restoring horizontally or vertically blurred images. By analyzing motion blur characteristics in images and employing sophisticated image processing algorithms including Wiener filtering and Richardson-Lucy deconvolution, we can effectively reduce image blurring and enhance visual clarity. This approach carries important implications for improving image quality, enhancing visual experiences, and enabling diverse applications across multiple domains including medical imaging and surveillance systems.

Overall, this research provides a systematic approach to motion-blurred image restoration, with strong theoretical support and reference from "Removing Camera Shake from a Single Image" (SIGGRAPH 2006). The implementation typically involves MATLAB or Python code utilizing image processing toolboxes for PSF estimation and iterative deconvolution operations. We hope our work contributes meaningfully to both academic research and practical applications in related fields.