IEEE Trans Image Process - Robust web image/video super-resolution.

Tópicos

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Resumo

This paper proposes a robust single-image super-resolution method for enlarging low quality web image/video degraded by downsampling and compression. To simultaneously improve the resolution and perceptual quality of such web image/video, we bring forward a practical solution which combines adaptive regularization and learning-based super-resolution. The contribution of this work is twofold. First, we propose to analyze the image energy change characteristics during the iterative regularization process, i.e., the energy change ratio between primitive (e.g., edges, ridges and corners) and nonprimitive fields. Based on the revealed convergence property of the energy change ratio, appropriate regularization strength can then be determined to well balance compression artifacts removal and primitive components preservation. Second, we verify that this adaptive regularization can steadily and greatly improve the pair matching accuracy in learning-based super-resolution. Consequently, their combination effectively eliminates the quantization noise and meanwhile faithfully compensates the missing high-frequency details, yielding robust super-resolution performance in the compression scenario. Experimental results demonstrate that our solution produces visually pleasing enlargements for various web images/videos.

Resumo Limpo

paper propos robust singleimag superresolut method enlarg low qualiti web imagevideo degrad downsampl compress simultan improv resolut perceptu qualiti web imagevideo bring forward practic solut combin adapt regular learningbas superresolut contribut work twofold first propos analyz imag energi chang characterist iter regular process ie energi chang ratio primit eg edg ridg corner nonprimit field base reveal converg properti energi chang ratio appropri regular strength can determin well balanc compress artifact remov primit compon preserv second verifi adapt regular can steadili great improv pair match accuraci learningbas superresolut consequ combin effect elimin quantiz nois meanwhil faith compens miss highfrequ detail yield robust superresolut perform compress scenario experiment result demonstr solut produc visual pleas enlarg various web imagesvideo

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