I don't care about TV

本文探讨了大学教育的目的不仅仅是娱乐和休闲,更重要的是获得良好的教育,为将来的职业生涯做准备。作者阐述了自己对大学教育的看法,认为其核心目标在于获取知识、技能和经验,以实现个人职业目标。
 

我不关心电视节目
shows
节目
 channels
频道
 tv
电视

Thereare many different types of shows and channelsonAmerican TV. There'stalk shows, news shows. .mp3Imean some of themaregood some of themarenot so good. Butanyway, some of usareat university to study。Idon't think Ian understandsthis! 

I think Ian came touniversity towatch TV and listen tomusic andgo traveling andhave fun.Well,I didn't come touniversityto watch TV and havefun. 

Icame to university togeta really good educationandto climb my wayupthe corporate ladder and to someday rule theworldand make a lotofmoney and have atrophywife.

我来到大学获得一个很好的教育,自己爬职位升迁,总有一天,统治世界,赚了很多钱,有一大堆的妻子。

trophy ['trəufi] 想启用英文朗读功能吗?请先安装flash插件!详细»

n. 奖品;战利品;纪念品

vt. 用战利品装饰

adj. 显示身份的;有威望的

ladder ['lædə] 想启用英文朗读功能吗?请先安装flash插件!详细»

n. 阶梯;途径;梯状物

vt. 在……上装设梯子

vi. 成名;发迹



1. Import Required Libraries - Hint: You'll need numpy for numerical operations, matplotlib.pyplot for visualization, scipy.fft for Fourier transforms, scipy.optimize for the minimization algorithm, and skimage for phantom generation and SSIM calculation. - Don't forget to import structural_similarity from skimage.metrics for quantitative comparison. 2. Define Helper Functions - verify_image function: - This function should check if an image is valid (not all zeros and has sufficient contrast). - If invalid, return a fallback image (e.g., the IFFT result) to ensure your code doesn't crash. - Always normalize images to the [0.05, 0.95] range for consistent visualization. - total_variation function: - Total variation measures image "smoothness" by summing absolute gradient values. - Calculate gradients along both x and y axes using np.diff(). - Sum the absolute values of these gradients to get the total variation. 3. Create the Original Phantom - Use skimage.data.shepp_logan_phantom() as your test image (a standard for medical imaging tests). - Resize it to your desired size (e.g., 64x64 or 128x128) using skimage.transform.resize(). - Enhance contrast slightly (e.g., scaling and clipping) to make features more distinct. - Always visualize the phantom with plt.imshow() using a grayscale colormap. 4. Generate Full K-Space - K-space is the Fourier transform of the image – it represents the image in the frequency domain. - Use scipy.fft.fft2() for 2D Fourier transform, but remember to apply ifftshift() first to center the frequencies. - Apply fftshift() after the transform to properly center the k-space data. - K-space will be complex-valued (contains both magnitude and phase information). 5. Create Undersampling Mask - The mask determines which k-space points we "measure" (1) and which we discard (0). - Start with a random mask using np.random.rand() and a threshold based on your undersampling factor. - Always fully sample the central region (most important for image contrast) using array slicing. - Calculate the mask center dynamically using img_size // 2 so it works for any image size. - Verify your mask by counting the number of sampled points (np.where(mask.flat)[0]). 6. Generate Undersampled K-Space Data - Apply the mask to full k-space using element-wise multiplication (*). - Add a tiny amount of noise (e.g., 1e-6 * np.random.randn()) to simulate real-world measurement noise. - Extract the measured values as a 1D vector using the mask indices (these are your "y" measurements for CS). - Verify the length of your measurement vector matches the number of sampled points in the mask. 7. Perform Basic IFFT Reconstruction - To get an image from k-space, use the inverse Fourier transform: ifft2(). - Remember to apply ifftshift() before the inverse transform and fftshift() after, just like with the forward transform. - Take the absolute value of the result (since we only care about magnitude for visualization). - Use your verify_image() function to ensure the result is valid. 8. Define Compressed Sensing Objective Function - Your objective function should combine two terms: 1. Data fidelity: How well your reconstruction matches the undersampled measurements (use squared error: sum((predicted - measured)**2)). 2. Regularization: Total variation to encourage natural image structure (scale it with a small weight like 0.0005). - To calculate predicted measurements: convert your reconstructed image to k-space, then extract values using the mask indices. 9. Run CS Optimization - Use scipy.optimize.minimize() with method='L-BFGS-B' (efficient for large problems). - Start with a good initial guess: the IFFT reconstruction flattened to a 1D array. - Use reasonable optimization parameters (e.g., maxiter=80, gtol=1e-4) to balance speed and accuracy. - The result will be a 1D array – you'll reshape it back to 2D in the next step. 10. Process and Verify CS Result - Reshape the optimized 1D array back to your image size (e.g., 64x64). - Use your verify_image() function with the IFFT result as fallback. - This step ensures you have a valid image even if the CS optimization struggles. 11. Display All Results - Create a figure with three subplots: original phantom, IFFT reconstruction, and CS reconstruction. - Use plt.subplot() to arrange them horizontally. - Ensure all use the same colormap (cmap='gray') and intensity range (vmin=0, vmax=1) for fair comparison. Add clear titles to each subplot. 12. Calculate and Compare SSIM Scores - Use structural_similarity from skimage.metrics to quantify image quality. - SSIM ranges from 0 (worst) to 1 (perfect match) – your CS result should score higher than the IFFT result. - Print both scores with clear labels for easy comparison.
最新发布
09-22
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值