کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
527157 869296 2011 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Learning-based super resolution using kernel partial least squares
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Learning-based super resolution using kernel partial least squares
چکیده انگلیسی

In this paper, we propose a learning-based super resolution approach consisting of two steps. The first step uses the kernel partial least squares (KPLS) method to implement the regression between the low-resolution (LR) and high-resolution (HR) images in the training set. With the built KPLS regression model, a primitive super-resolved image can be obtained. However, this primitive HR image loses some detailed information and does not guarantee the compatibility with the LR one. Therefore, the second step compensates the primitive HR image with a residual HR image, which is the subtraction of the original and primitive HR images. Similarly, the residual LR image is obtained from the down-sampled version of the primitive HR and original LR image. The relation of the residual LR and HR images is again modeled with KPLS. Integration of the primitive and the residual HR image will achieve the final super-resolved image. The experiments with face, vehicle plate, and natural scene images demonstrate the effectiveness of the proposed approach in terms of visual quality and selected image quality metrics.

Figure optionsDownload high-quality image (72 K)Download as PowerPoint slideResearch Highlights
► Kernel partial least squares (KPLS) method is used for image super resolution.
► The relationship between high- and low-resolution residual images is established to achieve an improved result.
► A two-step learning-based procedure is implemented.
► The effectiveness of the proposed method is demonstrated with experimental results.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Image and Vision Computing - Volume 29, Issue 6, May 2011, Pages 394–406
نویسندگان
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