Image deblurring

Multimedia is ubiquitous and the application of digital imaging is prolific, yet environmental conditions and hard ware limitations may adversely affect image quality. Advanced techniques such as image enhancement, deblurring, denoise, and super resolution have been developed to improve image quality post-digitization. Image enhancement is primarily concerned with problems caused by overexposure, underexposure, poor photographic technique, and optical noise. A. Gorai and A. Ghosh proposed a method based on a heuristic algorithm to enhance images by adjusting brightness and contrast. Lighting problems are resolved effectively through this method, yet there are limitations when applied to blurred images, i.e., images with defocus blur, motion blur, handshake blur, or fog blur. For those kinds of blurred images, we need a specific technique of image deblurring to apply. As a result, this research is primarily concerned with (a) image enhancement: improving upon the objective function and transformation function proposed by A. Gorai and A. Ghosh and (b) image deblurring: a proposed method to estimate blur kernel and its application towards image deblurring. This paper proposes a blind deblurring method needing to predict a blur kernel in our own way. The color distribution of edge is more distinct in a clear image than in a blurred image. A filter is proposed to make edges in a blurred image clearer for use as a reference image. The blur kernel is estimated from this reference image. The blurred image is then deconvolved with the estimated blur kernel to introduce a latent image.