Image Deconvolution using an Alternating Minimizer

When creating computer vision applications, it is important to have a clear image of what is represented such that further processing has the best representation of the underlying data. A common factor that impacts image quality is blur, caused either by an intrinsic property of the camera lens or by introducing motion while the camera’s shutter is capturing an image. Possible solutions for reducing the impact of blur include cameras with faster shutter speeds or higher resolutions; however, both of these solutions require utilizing more expensive equipment, which is infeasible for instances where images are already captured. This thesis discusses an iterative solution for deblurring an image using an alternating minimization technique through regularization and PSF reconstruction. The alternating minimizer is then used to deblur a sample image of a pumpkin field to demonstrate its capabilities.