Photo Deblur Studio

Photo Deblur Studio applies advanced deconvolution-based algorithms to reduce blur in digital photographs. It supports non-blind deconvolution when a blur kernel is provided and blind deconvolution when the kernel is unknown. Users can process full images or targeted regions via an optional mask. The tool emphasizes preserving natural textures while sharpening edges, minimizing halos and noise amplification. It also supports batch processing and non-destructive previews to align edits with original data.
Conceptually, blur is modeled as a convolution of the original scene with a point-spread function (PSF). The tool estimates the PSF or accepts a user-supplied one, then iteratively solves the inverse problem using regularized deconvolution and edge-preserving priors. It includes optional denoising, contrast adjustment, and mask-based constraints to limit edits to regions of interest. Outputs are delivered in the input color space (typically sRGB) and can match the original resolution and format.
Who benefits: professional photographers, photo editors, archivists, designers, and researchers who need clearer detail for assessment, print, or publication. Unique value: robust algorithms, region masking, and artifact control that reduce over-sharpening while retaining natural look. Core features include input image, optional mask, strength control, PSF estimation, denoising, and flexible export. Use cases cover motion blur removal, defocus recovery, low-light sharpening, and archival restoration, where preserving authenticity matters.