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Abstrak :
Dengan semakin lengkapnya fasilitas yang ada pada perangkat mobile, termasuk disediakannya kamera pada perangkat tersebut, salah satu yang dapat dimanfaatkan dari fasilitas ini adalah memotret obyek dan melakukan upload untuk keperluan mobile learning. Pada proses pemotretan, sangat dimungkinkan terjadi blur akibat pengguna kurang stabil dalam memegang perangkat mobile yang kurang stabil. Agar informasi yang terdapat pada gambar tetap dapat disampaikan dengan baik pada pengguna mobile learning lainnya, maka diperlukan proses image enhancement, salah satunya adalah deblurisasi. Selain deblurisasi, diperlukan juga antara lain proses resize gambar agar sesuai dengan ukuran layar pengguna. Namun proses tersebut harus tetap memperhatikan informasi yang berada pada gambar tersebut. Hal itu dimungkinkan dengan menerapkan algoritma seam carving pada saat adaptasi ukuran gambar. Dari hasil penerapan modul-modul deblurisasi dan seam carving pada mobile learning, didapatkan perbandingan kualitas antara gambar sebelum deblurisasi dan setelah deblurisasi. Waktu total yang diperlukan untuk me-load sebuah gambar dihitung, dan dibandingkan dengan nilai obyektif kualitas suatu gambar, yaitu Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) dan Blurred Signal to Noise Ratio (BSNR) ......Nowadays, a lot of mobile devices are equipped with more feature other than telephone and SMS, such as camera, enable users to do more with their devices. It includes uploading pictures taken by camera on their phone for mobile learning purpose. It is widely possible that blurs occur in the process of taking the picture. In order to enhance image information to users, one of common method to improve image quality is deblurring. Beside deblurring, adaptation is also needed to deliver image based on user?s phone screen size. It can be achieved by implementing seam carving algorithm when resizing the image. Objective image quality can be obtained by computing MSE and PSNR from original and deblurred images. Total response time to load an image is also identified and then being compare to objective image quality values.

Abstrak :
At no other time in human history have the influence and impact of image processing on modern society, science, and technology been so explosive. Image processing has become a critical component in contemporary science and technology and has many important applications. This book develops the mathematical foundation of modern image processing and low-level computer vision, and presents a general framework from the analysis of image structures and patterns to their processing. The core mathematical and computational ingredients of several important image processing tasks are investigated. The book bridges contemporary mathematics with state-of-the-art methodologies in modern image processing while organizing the vast contemporary literature into a coherent and logical structure.

Abstrak :
When we use a camera, we want the recorded image to be a faithful representation of the scene that we see, but every image is more or less blurry. In image deblurring, the goal is to recover the original, sharp image by using a mathematical model of the blurring process. The key issue is that some information on the lost details is indeed present in the blurred image, but this hidden information can be recovered only if we know the details of the blurring process. Deblurring Images: Matrices, Spectra, and Filtering describes the deblurring algorithms and techniques collectively known as spectral filtering methods, in which the singular value decompositionr a similar decomposition with spectral properties as used to introduce the necessary regularization or filtering in the reconstructed image. The concise MATLAB implementations described in the book provide a template of techniques that can be used to restore blurred images from many applications.